Cloud Configuration Automation via Machine Learning Compliance
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Solution Overview
Problem
Migrating to cloud-based deployments is challenging due to the need for manual configuration of various applications and modules, which is time-consuming and error-prone, especially when regulatory compliance across multiple jurisdictions is involved.
Innovation Solution
A configuration device with a data processing module, modelling module, and loading module that uses machine learning to evaluate configuration workbooks for compliance, generate recommendations, and automate the population of loading templates for cloud-based functionality, thereby streamlining the configuration process and ensuring regulatory compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration operations are performed to migrate to cloud-based deployments, then compliance with regulatory requirements can be achieved, but the process becomes very time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical configuration operations with an automated system comprising a data processing module, modeling module with machine learning capabilities, and loading module. This substitution eliminates human labor in the configuration process while maintaining compliance accuracy through automated validation and generation of configuration files.
Solution Approach 2:
The system enables self-service automation where the configuration device autonomously compiles datasets, evaluates compliance using machine learning models, generates recommendations, and populates loading templates without requiring manual intervention. The system serves itself by automatically iterating through compliance evaluation and configuration generation.
2Ease of manufacture
If manual advanced loads are performed for each module during migration, then configuration can be completed, but the effort required is extremely high (approximately 260 hours per module)
Solution Approach 1:
The system performs preliminary actions by pre-compiling datasets containing loading templates, configuration workbooks, requirements information, and mapping data before the actual configuration process. The machine learning model is pre-trained to evaluate compliance, enabling rapid automated configuration generation without manual advanced loads for each module.
Solution Approach 2:
The patent transforms the configuration process from manual parameter-by-parameter setting to automated parameter generation. The loading module automatically populates loading templates with configuration parameters based on evaluated compliance results, changing the state from incomplete manual configuration to complete automated configuration.
3Adaptability or versatility
If manual configuration operations are performed, then flexibility in handling different regulatory jurisdictions is maintained, but errors are more likely to occur and additional delays are created
Solution Approach 1:
The configuration device provides universal functionality to handle multiple regulatory jurisdictions through a single automated system. The data processing module compiles comprehensive datasets including requirements information from different jurisdictions, and the machine learning model evaluates compliance across all jurisdictions uniformly, eliminating jurisdiction-specific manual errors.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously evaluates configuration workbooks for compliance and generates recommendations for improvement. This iterative feedback loop ensures configuration accuracy by automatically identifying and correcting errors before final deployment, maintaining adaptability across different regulatory requirements.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support configuration of cloud-based functionality. A configuration device is provided and includes a data processing module, a modelling module, and a loading module. The data processing module provides functionality for compiling information for use in configuring the cloud-based functionality in a requirements compliant manner. The modelling module may include various machine learning modules configured to evaluate configuration workbooks for compliance with requirements specified by a user. The modelling module may output recommendations for improving compliance of the configuration workbooks and appropriate changes may be made. The loading module may be configured to obtain templates applicable to the cloud-based functionality being configured and to extract appropriate data from the (updated) configuration workbooks. The extracted data may then be loaded into the obtained templates for use in configuring the cloud-based functionality in a regulatory compliant manner.